Face mask recognition, as a subset of computer vision, has gained significant attention due to the COVID-19 pandemic. The ability to detect face masks in real time is essential for ensuring public safety in high-risk environments such as airports, hospitals, and public transport systems. This project aims to build an efficient, real-time face mask recognition system using deep learning models and computer vision algorithms.
The system leverages state-of-the-art models like MobileNetV2, ResNet, and VGG16, which are known for their balance between performance and computational efficiency. These models, when combined with object detection algorithms like YOLO (You Only Look Once) and SSD (Single Shot Multibox Detector), create a robust framework for detecting masks with high accuracy. The project also delves into the practical implementation of these models, addressing challenges like varying lighting conditions, occlusions, and scalability for large datasets.
In this system, preprocessing steps such as face detection and segmentation are applied before the recognition task. The project further explores how techniques like transfer learning can be employed to fine-tune pre-trained models, thus accelerating the development process while maintaining high accuracy. By examining these methodologies, this project provides a comprehensive guide to building and deploying face mask recognition systems in real-world environments.